Predicting outcomes from measures of group cooperation with applications in traffic alert and control

1. A method for automatically measuring group cooperation and for modifying a group outcome in response to the measurement of group cooperation, the method comprising: dynamically updating a central model of focus of attention of each member of a group of vehicles that are engaged in driving and communicating data about focus of attention within a communication network; generating an interaction graph that tracks, over time, which of the members have interacted with each other and over what linkages of the interaction graph the interactions have occurred; determining a cooperation index of the group based on the central model of focus of attention and the interaction graph, wherein the cooperation index is a global measure of group cooperativeness; comparing said cooperation index to historical data to predict the group outcome for the group of vehicles; determining that the predicted group outcome has significant negative consequences; and alerting, via the communication network, the group of vehicles engaged in the activity requiring cooperation, upon determining that the predicted group outcome has significant negative consequences, wherein: the alerting comprises communicating an exceedance over a communications channel of the communication network to the group of vehicles and to a traffic alert and control system connected to the communication network, wherein the exceedance is configured to increase the cooperation index by causing a change in a timing of at least one traffic signal of the traffic alert and control system affecting the vehicles and by preemptively engaging automatic guidance and operation capabilities of at least one of the vehicles.

2. The method of claim 1 , wherein the comparing of the cooperation index to historical data comprises comparing a determined value of total cooperation index to a regression of the historical data.

3. The method of claim 1 , wherein: the step of dynamically updating the model of focus of attention is carried out with a focus of attention monitoring module, embodied on a non-transitory computer readable medium, executing on at least one hardware processor; the step of generating said interaction is carried out with an interaction graph module, embodied on the non-transitory computer readable medium, executing on the at least one hardware processor; the step of determining the cooperation index is carried out with a feature extractor and supervised learning module, embodied on the non-transitory computer readable medium, executing on the at least one hardware processor; the step of comparing the determined value of total cooperation index to historical data to predict the group outcome for the group of vehicles engaged in the activity is carried out with the feature extractor and supervised learning module, embodied on the non-transitory computer readable medium, executing on the at least one hardware processor; and the step of alerting the group of vehicles is carried out with the feature extractor and supervised learning module, embodied on the non-transitory computer readable medium, executing on the at least one hardware processor.

4. The method of claim 1 , wherein the alerting further comprises causing at least one of a mechanical vibration or an electrical stimulus in a hand or foot control of at least one vehicle of the group of vehicles.